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EURASIA Journal of Mathematics Science and Technology Education ISSN 1305-8223 (online) 1305-8215 (print)

2017 13(1):35-59 DOI 10.12973/ eurasia.2017.00603a

© Authors. Terms and conditions of Creative Commons Attribution 4.0 International (CC BY 4.0) apply.

Correspondence: Mohamed Hamada, University of Aizu, Ikkimachi, 965-8580 Aizuwakamatsu, Japan.

[email protected]

An Interactive Learning Environment for Information

and Communication Theory

Mohamed Hamada University of Aizu, JAPAN

Mohammed Hassan University of Aizu, JAPAN

Received 22 March 2016 ▪ Revised 27 May 2016 ▪ Accepted 6 July 2016

ABSTRACT

Interactive learning tools are emerging as effective educational materials in the area of

computer science and engineering. It is a research domain that is rapidly expanding

because of its positive impacts on motivating and improving students’ performance during

the learning process. This paper introduces an interactive learning environment for teaching

and learning information and communication theory and related courses. The environment

integrates several modules to meet the students’ different learning styles. It contains a

movie-like module, an animated hypertext introductory module that fully explains the

fundamental concepts of information and communication theory. Besides those important

learning modules, it also contains a self-assessment module that contains a set of

interactive tests and examinations. Learners can use the environment as a standalone

application or as an applet from within any web browser. Some evaluation experiments and

comparative analyses of the results were performed to measure the performance of our

environment in the classroom.

Keywords: Interactive Learning; Learning Technology; Virtual-learning Environment;

Information and Communication Theory.

INTRODUCTION

One of the most significant discussions regarding advances in hardware and software is their

ability to support interactive learning tools that can enhance the learning process and their

potentials to support learning requirements of an individual learner.

The term interactive learning tool is commonly referred to a computer-based

environment that integrates a group of learning modules such as audio, video, animations,

simulations, graphics, text, etc. They enable students to experience their learning topic in an

easy and expressive manner. Interactive learning systems have become a common practice in

computer engineering education, and it received a considerable critical attention. (Boluda et.al,

2006; W. Chen, 2007; Haffner, 2006; Hamada & Sato 2006; Mohamed., 2008; Hamada, 2007;

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Martinez-Jimenez et.al, 2006; Li, 2006; Rodger, 2006; Cadenas, 2015) are some of the recent

examples of interactive learning environments. In these learning tools, hypertexts, graphics,

animations, and simulations are widely used. Most of these studies have shown that using

interactive learning systems can improve the learning outcome and enhance the students’

motivation. However, while designing an interactive learning environment, considering

students learning styles will serve as a catalyst in improving the effectiveness of the system.

A considerable number of literatures have been introduced (e.g. (Herman, 1990; Felder,

1988; Kolb, 2014; Briggs Myers, 1980; Hamada, Muhammed, & Tufan, 2014) in order to realize

the learning preferences of students. Among them, the simpler and easier to implement (see

(Felder & Spurlin, 2005)) is Felder-Silverman (Felder & Spurlin, 1988). It classifies learners into

4 groups, just as sequential vs. global, visual vs. verbal, sensing vs. intuitive, and active vs.

reflective. Logical steps and sequential details are the basis for sequential learners to

understand concepts. Global learners prefer to understand a concept as a whole and in big

jumps. Diagrams, images, demos, movies, and tables are the preferred learning methods of

visual learners. Written and spoken words are the best learning methods for verbal learners.

Sensing learners try to understand concepts usıng their several sense organs. Intuitive learners

try to understand concepts by discoverıng facts and the relationships that connect them. Active

learners understand concepts through the idea of “I do, I learn”. Reflective learners tend to

think about facts as a preferred method of learnıng. In (Rosati, 1998), an empirical study of

Felder-Silverman model was reported in order to specify what kind of learning preferences

that engineering students possess. The result of this study outlined that engineering students

tend to have visual, sensing, sequential, and active learnıng preferences.

Nonetheless, previous studies on interactive learning have not paid much emphasis on

students’ learning preferences. Therefore, in this research, we proposed to develop and

State of the literature

Students attend classes and taught in one group irrespective of their learning preferences.

Many professors teach their students theoretically in some courses that require students’

participations to better understand the subject.

Most of the existing literatures that developed and interactive learning environment for

information and communication theory did not cover the entire course/subject; they only

focused on some selected topics.

Contribution of this paper to the literature

This paper introduces a complete design and use of interactive learning environment for

computer science and engineering courses. It aims to contribute to this growing area of research

in the interactive learning environment by exploring the concept of information and

communication theory and related courses. The environment was developed and tested on

students taking this course for three consecutive years. The understanding and performance of

the students were improved when using this learning environment.

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37

evaluate an interactive learning environment that takes students’ learning preferences into

consideration in learning the concept of information and communication theory.

The concepts of information and communication theory have a very wide range of

practical applications. Owing to its abstract nature, these concepts are normally taught by

using classroom lectures in a usual style, which makes studying such concepts a less

motivating task for engineering students (because of their strong active learning preferences).

Our interactive learning framework is developed mainly to deal with this problem and give

supports to engineering students especially those with active learning preferences. The

proposed environment is suitable for supporting active learners in information and

communication theory topics, and also in many other related fields some of which are: image

processing, data compression, telecommunications, error detection and correction courses, etc.

Such topics cover a variety of concepts, like communication channels, coding/decoding

techniques, source of information, technıques for detection and correction of errors, mutual

information, source entropy, etc. The modules of the system are implemented in Java and then

ıntegrated into a unique environment that uses Java2D interface (Java2D of Sun

Microsystems). The system is web-based, very portable, with a rich and easy to understand

interface that has many desirable properties for motivating students towards learning. These

characteristics make our environment a useful interactive learning tool for engineering

students that can provide them with an opportunity to advance their understanding of the

target subjects.

In developing our environment, we paid special attention to the learning model of active

constructionism (Hadjerrouit, 2003; Perkins, 1996) and the principles of its design, which

includes:

1) Learners should act as knowledge constructors, not as passive receiver from teachers

2) The learning process should contain embedded assessment procedures that consider

learners’ individual orientations.

Benefits of using interactive learning systems in computer engineering education,

includes, but, of course, not limited to the following:

• Allow using more brain sensors, which in turn will encourage deep reflective thinking.

• Allow creating an enhanced learning process for individual learners.

• Allow learners to behave as designers by using computer simulations to analyze their

topic.

The overall structure of the paper takes the form of seven sections, including this

introductory section. Section two of the paper discussed some related works. Section three is

concerned with the introduction of our interactive learning environment and all of its modules.

The learning theory that provides a theoretical basis for our system will be discussed in section

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four. Section five examines the results of our experiments. Discussions of research findings are

presented in section six. Lastly, section seven of the paper contains the conclusion that

summarizes our findings and areas for future work are discussed.

RELATED WORK

Based on our intensive search, and the use of many keywords, with several search

engines such as MSN, Google, Yahoo, and Wikipedia, but so far, no previous work that

develops a similar single tool for the entire area of information and communication theory

course. However, little attention has been paid to develop a system that can simulate just one

topic in information and communication theory. For example, a number of authors (Ang; W.

Chen, 2007; Ligus; Thanh) have introduced a simple simulation of the Huffman coding. In

comparison with the Huffman algorithm simulation of our system (that represents only one

of the many modules in our system), we found that this new system is more educative and

interactive than all of them. In these Huffman’s code simulations, learners can only (passively)

watch a simulation of how Huffman algorithm works. While in our learning environment,

learners are able to build the coding steps one-by-one or as a whole. This provides a more

interactive learning environment and makes students more engaged in the learning process.

In addition, they can obtain an immediate and a direct assistant at any stage during the

learning session by navigating on the “introduction” module or simply by clicking on either

the “Help” or the “Hint” button.

In (Mackay), a collection of information theory simulation programs was introduced

such as Huffman, Hamming, and arithmetic coding. Unlike our all-in-one simulation, they are

written as separate programs in different programming languages such as TCL, Python, and

C. So they need a separate compilation and execution. Hence, they are not portable and need

more efforts from the user (to run and use them) than our environment. In addition, our

environment is more interactive and allows the user to get more engage in the simulation

process rather than just sit and watch in a passive style.

INTERACTIVE LEARNING ENVIRONMENT

For the purpose of integrating different desirable components of the subject, the

interactive learning environment is designed to have six different modules, which are

assembled into a unified user-friendly interface to enable the students to access all the modules

in an easy way. The modules are as follows: First, is an animated (movie-like) welcome module

that briefly introduces the environment to the learner. Second, is a hypertext introduction

module that introduces the basic concepts of information theory (in a power point presentation

style to make it easily accessible by learners), three simulation modules: a communication

model simulator, an image compression simulator, and text compression simulator. Finally,

an assessment module that contains a set of self-assessment tests designed carefully to assess

the learners’ performance during the learning process. The “welcome” and “introduction”

modules are designed to match the sequential learning preferences of students. The


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simulators’ modules are more suitable for students with sensing and active preferences (such

as engineering students). The environment is implemented in Java and integrated into Java2D.

It is worth mentioning that using Java2D as an interface for the environment can enhance the

visual clarity of the contents and can add extra functions like texture change, memory

monitoring (as shown in the right side of Figure.1), and a possibility for printing from within

the environment (as shown in the bottom side of Figure.1). In the sequel, we will introduce all

the system’s modules. Subsection 3.2 is based on our previous work (Hamada, 2007b).

Figure.1

Welcome and Topics “Introduction” Modules

The first two modules of the learning system are dealing with the basic concepts of

information and communication theory. One module introduces a movie-like simple

introduction of the course. The second one introduces an intensive “introduction” in a

hypertext format that uses a PowerPoint presentation style. Students can easily use the

hyperlinks in this module as a navigation method through the course concepts. An audio

narration is combining the presentation in order to make it easier for students who may have

some difficulties in reading the text. It also can support students with audio/visual

preferences. The introduction also is available in both English and Japanese as a bilingual

presentation.

Simulator for the Communication Model

This section introduces the simulator for the basic communication model. It simulates

the basic operations that describe the steps of message transmission from source to destination.

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Figure.1 shows an overview of interface of the simulator, which is based on our previous work

(Hamada, 2007).

Figure. 2

Figure.3

Therefore, more detailed description of the simulator can be found in (Hamada, 2007).

Figure.2 and Figure.3 present the snapshots of the simulator working on an example that


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simulates the transfer of the message “Hello World” (Figure.2) using Huffman coding

technique (Figure.3).

Image Compression Simulator

The image compression simulator is concerned with a bitmap (.bmp) image format. It

can compress a bitmap (.bmp) image using several image compression techniques such as 8-

bits, 8-bits with run length code (RLE), 16-bits, and 24-bits. Bitmap compression technique was

chosen because of its wider area of applications. For built-in graphics subsystems, many

graphical user interfaces use bitmaps; See, for example, the OS/2 platforms' GDI subsystem

and Microsoft Windows. Also, many other image formats, such as PNG, JPEG, GIF, and TIFF

uses bitmap format.

Figure.4

The image compression simulator interface is set out in Figure.4. To facilitate the

learning process, there are several additional functions namely: make image, test, and help

were integrated into the simulator. The help function gives a comprehensive introduction to

the image compression simulator, its underlying algorithms, and its use. It is useful for new

users as a learning starting point. It is given in bilingual (English/Japanese) with easy to use

hyperlinks to each learning object. The “Make Image” function in Figure.5 below can help

learners to understand “how images can be created from a group of pixels”. First, the learner

can set the desired width and height of the images he wants to create. Next, select the desired

bitmap algorithm (like 8-bits, 8-bits (RLE), 16-bits, or 24-bits) to create the image. Based on the

given width w and the height h, a set of empty squares (= w * h) will appear as provided in

Figure.5 above. The learner can then fill in the empty squares with hexadecimal values for

each pixel or can use the “Random” button to randomly fill in the values for all pixels. Finally,

the learner can use the “Make Image” button to create the corresponding image. The “Clear”

button can be used to clear the squares and start the creation of a new image.

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Figure.5

By clicking on the “Load” button in the main image in the simulator interface, the user

can load a bitmap image that he/she wants to convert into another bitmap representation. The

loaded image and its information will appear in the upper area of the image simulator window

(See Figure.4 for an example of a loaded 24-bit bitmap image). In the shown example, since

the loaded image is in 24-bit format, the “24-bit” button will be disabled and the other buttons

(“8-bit”, “8-bit (RLE)”, and “16-bit”) will be enabled. By clicking on one of these three enabled

buttons (for example “8-bit (RLE)” button as in the example of Figure.6) the loaded image will

be transformed (compressed) into the corresponding format. The compression result will be

shown in the lower area of the simulator window as displayed in Figure.6.

Figure.6


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In addition to the resulting image and its related information, two other windows will

appear automatically, one window will give a detailed result of the image pixels, the other

window will give detailed values (in decimal and hexadecimal representations) of the image

colors (the color of each pixel) included in the compressed image as summarized in the

example of Figure.7 below.

Figure.7

The test function is important for helping learners to understand the underlying bitmap

image compression algorithms. When the learner clicks on the “Test” button, a pop-up

window, showing the underlying algorithms, will appear. The learner can select the desired

algorithm she/he wants to learn. For example selectıng “24-bit to 16-bit” a new window will

appear as in Figure.8. First, the learner will be asked to input (in the text area) the RGB (Red,

Green, Blue) values for the 24-bits image that she/he wants to convert to 16-bits. A set of empty

colored (Red, Green, Blue) squares will appear (see Figure.8). Every line represents the

inputted data with different representations. The “Hint” button can be clicked at any time to

give quick information about what the student should do at this moment and how she/he can

do it. The student can also click on the “Random input” button to get a quick random example.

By clicking on the “OK” button, the student can check her/his result. If the student still cannot

get the correct answer, then she/he may click on the button “Answer” to get the correct answer

automatically.

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Figure.8

Text Compression Simulator

The text compression simulator is concerned with Lempel-Ziv (LZ) text compression

algorithms. A. Lempel and J. Ziv introduced the lossless data compression algorithms LZ77

and LZ78 in 1977 (Ziv and Lempel, 1977) and 1978 (Ziv and Lempel, 1978) respectively. They

are the foundations for many other LZ variations, such as LZW, LZSS, LZMA, etc. LZ methods

do the data compression by replacing multiple occurrences of data with some references to

one copy of the data that exist earlier in the initial data stream. The matching process is

encoded with a double of numbers known as “length-distance” numbers. To place matches,

the encoder has to keep the reference of some quantity of the most recent information, for

example, the final 2, 4, or 32 kB. The framework in which this information held is known as a

“sliding window”, that is the reason for calling the LZ77 the “sliding window” compression.

We decided to adopt LZ compression techniques because of its practical importance.

Some applications of LZ compression techniques are GIF, TIFF, V.42 bis modem compression

standard, PostScript Level 2, and the well-known ZIP format for data compression.

Figure.9 shows the main interface window of the text compression simulator. The

learner can either input text (to be compressed) directly into the text input area (in the upper

part of the simulator) or load a text file (using the “Load” button) to compress it. The “Clear”

button can be used to clear the current data and start a new text compression session.


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Figure.9

A comprehensive hypertext help window will appear when the user clicks on the “Help”

button. This help window will give a detailed information about the underlying LZ

compression algorithms and a detailed information about how to use the text simulator. An

example of inputted text is illustrated in Figure.9. After inputting the text, a typical session of

using the text compression simulator is as follows. The learner can set the desired “sliding

window compression” value using the slide bar (located along the right side of the simulator

window). The learner can now start to compress the inputted text by clicking on the

“Compress” button. Then the detailed compression process of the inputted string will appear

in a separate window as it appears in Figure.10 for an example. The figure shows the source

data, the output (compressed) data, and the compressed pattern, are given in a step-by-step

format. This allows the student to go through deep details of the LZ compression algorithm.

Figure.10

Similarly, learner can also input a compressed text (or load a compressed file) and click

on the “Decompress” button to decompress it. Then the decompression process will appear in

details (in a separate wındow).

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The test function is important to help the learner to understand the underlying LZ

compression algorithms. When the learner clicks on the “Test” button, a pop-up window, with

several options, will appear as given in Figure.11. The learner can set the desired “sliding

window compression” value using the slide bar. Next, the learner will be able to input some

text in the “In” area (stands for input). Then she/he can try to write the output in the “Out”

area (stands for output). If the inputted text was compressed, then the output text should be

its uncompressed version and vice versa. The student can check her/his answer by clicking on

the “Compress test” or the “Decompress test” buttons. If the student cannot make the correct

answer, she/he can try to click on the “Hint” button to get a quick comment that may help in

finding the answer. Otherwise, she/he can click on the “Compress data show” or

“Decompress data show” buttons to get the correct answer automatically.

Figure.11

The Learners’ Assessment Module

A selected group of tests with increasing level of difficulties is integrated into the system.

It contains many kinds of questions such as multiple choice, fill in the blank, etc. Students can

try the tests before, during, or after the learning sessions. Figure.12 shows the assessment

module’s interface.

Figure.12


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When the student selects a proper test from the interface window, the test description

will appear (e.g. its level, its prerequisites’ knowledge, its objectives, method of evaluation,

etc.). Using the navigation buttons, students can go through the list of questions. Students can

also check their current score at any time throughout the test. During a quiz answer, students

may get some help by clicking on the “introduction” button to get more readings on the

courses that are related to the question.

LEARNING THEORY

It is not always bad to think on which learning theory is preferable than the other, but

however, using an integrative approach is more important than arguing on the preferred

learning theory. This is because; such approach can integrate several instructional methods

that are necessary to effectively promote the students’ learning preferences. For instance,

literatures such as (Gagne, 1985; Merrill, 1983; Mudur, 2005; Reigeluth, 2013) contain the

following instructional design issues for content design.

• Classification of knowledge.

• Sequencing of content.

• Developing the required learning outcome and learning events.

Therefore, by taking these principles into account, we developed our learning

environment. The methodological approaches taken in the design process of our environment

are well explained in the following subsections.

Learning Design

The study uses learning design because of its representation of new possibilities for

improving the quality of the learning process within an e-learning environment (Britain, 2004).

Two things to note here are:

• Learning design (LD): The advantage of LD is that students learn in a better way if they

are engaged in some learning activities.

• Learning activities (LA): LA should be designed carefully in a workflow of learning to

facilitate the effectiveness of learning.

Following these ideas and the IMS-LD learning design specifications (IMS-LD Learning

Design Specifications) and (The Advanced Distance Learning Group; Dougiamas, 2002), our

environment is designed from a set of learning objects and a set of learning activities described

below.

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Objects

The following environment’s modules describe the learning objects.

1) The first learning object is the “Topics” component (represented by the “Welcome” and

“Introduction” modules). This is a collection of URLs and PowerPoint presentations that

describe the course concepts. Students can navigate through these concepts sequentially

or can select a specific concept from the main menu through the hypertext links.

2) A collection of examples that reflect the use of the information theory concepts in our daily

life. The main purpose of these examples is to motivate students by showing them the

relevance of the topics. Students can study these examples and see how they are connected

with the theoretical topics.

3) The communication model simulator is the learning object that enables the learners to

interactively build a communication process (from source to destination) and test how it

works in a detailed sequential order.

4) The image compression simulator is the learning object that facilitates the students to learn

how the image can be created and encoded. This can help to deepen their understanding

of the corresponding theoretical aspects of the subject.

5) The text compression simulator is the learning object that helps the students to learn how

text can be compressed and decompressed. This also can help to deepen their

understanding of the corresponding knowledge.

6) A group of tests is a learning object that helps them to assess their learning progress. The

tests are of different levels, starting from general and easy tests to more specific and

difficult ones. Students can try to answer the tests at any time during the learning session.

Activities

Based on our several years of experience in teaching information and communication

theory, we recommend students to respect the following sequence of learning activities.

1) Students are recommended to start by reading the examples in the introduction module.

At this level, a prior knowledge of the information theory concepts is not necessary. This

step may attract the students’ attention on how the subject is relevant to our daily activities.

The attention of the students and relevance of the subject are among the principles of the

ARCS model of motivation developed by Keller’s (Keller J. M., 1987).

2) Trying to answer some of the (easy) assessment tests, students can gain self-confidence,

which is also one of the principles of Keller’s ARCS motivation model (Keller J. M., 1987).

By now, students should be ready to start studying the theoretical aspects of the subject

through the topics modules.


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3) Therefore, students are now ready to try using the simulators. While using the simulators,

students are recommended to switch to the theoretical topics (in the topics module) for

proper understanding. While reading a certain topic, students are also recommended to

try the appropriate simulator and design a model for the concept they study and test it

with different input data. Such learning practice can be useful to deepen the knowledge of

the students and improving the learning progress.

4) For self-assessment (while they read the concepts and use the simulators), students are

encouraged to take the related test. Hence, their confidence on the learning process can be

upgraded.

The learning objects and a suggested workflow of the learning activities through the

system are given in Figure.13.

Figure.13

EVALUATION

Several experiments were performed to evaluate the effectiveness of our learning system

towards students’ motivation and performance. Motivational experiment was prepared and

carried out according to Keller’s ARCS motivational model (Keller J. M., 1987). An alternative

experiment was carried out to evaluate the students’ performance when using our

environment. For the purpose of estimating how students enjoyed this application, students’

feedbacks about the environment were collected and analysed. Lastly, a statistical survey on

the students’ grades in the past five years was carried out for analysis and comparison.

Motivation

Motivation plays a vital role in inspiring learning activities and enhancing the learning

process. A number of motivational models have been developed, such as ARCS model (Keller

J. M., 1987), Intrinsic Motivations (Malone, 1987), and Time Continuum Model of Motivation

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and Motivational Framework for Culturally Responsive Teaching (Wlodkowski, 1999).

Among these models, ARCS model has been used to evaluate motivations of learners in a

variety of learning settings, including E-learning environments and web-based distance

settings (Keller J. a., 2004), hypermedia contexts (Carson, 2006), and classroom face to face

setting (Small, 2004). Therefore, this model has been used in this study as a measure for

students’ motivation. ARCS model is based on four factors, namely, attention, relevance,

confidence, and satisfaction. In this paper, the word Attention will be used to refer to the

gaining and sustaining learners’ attention in a learning environment and can be reinforced

through some learning practices such as perceptual arousal (where gaining attention can be

achieved by offering novel and surprising activities). The term Relevance referred to the

degree that instructions meet learners’ needs and could be achieved through “familiarity” by

adapting instruction according to learners’ background. Confidence is the expectancy of the

learners for success and could be achieved through “success opportunities” (providing

different achievement levels), and “personal control” (providing feedback and support).

Finally, Satisfaction was referred to learners’ feeling about their accomplishments and could

be enhanced by providing “natural consequence” (providing opportunities to use newly

acquired knowledge in a real or simulating setting), and “positive consequences” (providing

feedback and reinforcement).

The four factors of the ARCS motivational model were the basis for designing our

environment. As for attention, a set of different attractive materials such as videos, simulators,

hands-on examples, etc., were integrated into the environment. These active materials also

matched with the active learning preferences of engineering students (Rosati, 1998) to form

the “familiarity” basis for the relevance part of ARCS model. The multilevel “test” component

of the environment gives a chance for success to answer quizzes irrespective of the learner’s

level. This can offer some degree of confidence to the learners satisfying the relevance part of

ARCS model. As for the satisfaction part of ARCS model, our environment provides a suitable

platform to simulate the acquired knowledge through its several simulators and hence provide

a “natural consequence” of the learning process.

To measure the effectiveness of our environment on the students’ motivation, an

experiment was carried out in the information theory classroom. A total of seventy-eight

students were divided into two equal groups (experimental and normal) with 39 students

each. Both groups attended the class regularly. However, in addition to their class attendance,

the experimental group used the environment as an additional learning tool. By the end of the

semester, a quantitative survey (based on ARCS model (Keller J. M., 1987) and on the study in

(Kebritchi, 2008)) was conducted. The interaction methods of the survey (shown in Table 1)

were adapted from (Kebritchi, 2008) where the validity and reliability were confirmed (based

on Cronbach’s alpha measure). The survey was conducted twice for each group, before and

after completing the class. Students can respond using one of the following 5 options: “no”,

“yes”, “maybe no”, “maybe yes”, and “don’t know”. The feedbacks of the normal group are

shown in Table 2 and Figure.14.


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Table 1. Survey quizzes based on ARCS motivational model.

Figure.14

Table 2. Responses from Normal group students.

It is clear from the table that majority of students in the normal group answered “Don’t

know” before taking the class. After taking the class, and things became clearer, the “Don’t

know” students are distributed almost equally in both sides “Yes” and “No”. We noticed that

there was no significant difference in the students’ motivation towards the class. The

responses of the experimental group are shown in Table 3 and Figure.15.

Attention

There is something interesting about this class that will capture my attention.

I believe this class will gain and sustain my interest.

I think that the variety of materials, exercises, illustration, etc., will help keep my attention on

this class.

Relevance

It is clear to me how this class is related to things I already know.

I believe that the information contained in this class will be important to me.

It is apparent to me how people use the information in this class.

Confidence

I think this class will be challenging, but neither too easy, nor too hard for me.

As I learn more about this class, I am confident that I could learn the content.

After working on this class for a while, I believe that I will be confident in my ability to successfully

complete all class assignments and requirements.

Satisfaction

I believe that completing this class will give me a feeling of satisfaction.

I believe that I will enjoy this class so much that I would like to know more about this topic

I will really enjoy completing assignments for this class.

Before

Answer No Maybe no Don’t know Maybe yes Yes

Average no. of students 5 6 14 8 6

After


Average no. of students 8 9 5 10 7

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Table 3. Responses from the Experimental group students.

Figure.15

As in the normal group, it is clear from the table that the majority of students in the

experimental group answered, “Don’t know” before taking the class. After taking the class,

and things become clearer, the “Don’t know” students are mostly shifted towards the “Yes”

side. This shift indicates a considerable amount of improvement in the students’ motivation.

It is also worth mentioning that the class attendance rate of the experimental group of students

was higher than that of the normal class attendance. This is another indication that confirms

increase in motivation among the experimental group of students.

Performance

Using our environment as shown by the following experiment can enhance the students’

performance. To distinguish between two possibilities (using and not using the environment),

a sample of one hundred students from the information and communication theory course

was divided into four groups with twenty-five students in each group. Inter-group

communications were highly restricted. Among the groups, only two groups were asked to

study selected topics using the environment for a period of one month. The other two groups

were asked to study the same topics within the same period without using the environment.

However at the end of the month, each group received 10 out of 40 selected exercises to answer

within a specific amount of time. A better performance from the groups using the environment

was observed. Exercises were redistributed among the groups for a second round of the

experiment. This was repeated by maintaining the groups in four rotations. In each rotation,

Before

Answer No Maybe

no

Don’t know Maybe yes Yes

Average no. of

students

4 8 13 7 7

After


Average no. of

students

3 7 4 13 12


53

the performance of the two groups that studied the topics using the environment was better.

In the average, at each time of the performance evaluation, those groups using the

environment can approximately achieve 79% of the correct answers while the other students

(studied without the environment) could achieve 51% of the correct answers.

Poll

A Simple opinion poll was carried out to test suitability and usability of our environment

as an active learning tool for information theory class. A mixed random sample of 180 students

from the last three academic years (2012-2013, 2013-2014, and 2014-2015) when the

environment was integrated into the course materials gave us their feedback through answers

to the poll’s questions.

Among the ten poll’s criteria are questions related to the environment’s functionality,

design, simulators usability, and how it can help in enhancing the information theory learning

process. Students were asked to evaluate the environment, based on these criteria, on a scale

of five points: 0 to 4 (from worst to best). The average points for each item are shown in Table

4.

Table 4. Evaluation result of the environment

The collected data revealed that many items get a high evaluation. Generally speaking,

students appreciated the environment as an active learning tool.

Statistics

To measure the effectiveness of using the environment on the students’ performance, a

survey for the students’ results on the last five years was carried out. The summary statistics

for the survey presented in Table 5 clearly reflects the positive effect of integrating our

learning environment with the information theory course.

M. Hassan et al.

54

Table 5. Students’ grades for five years

The course evaluation is based on regular exams, homework, and learning sessions. In two

academic sessions 2010-2011 and 2011-2012 before integrating the environment into the course,

we observed that the number of failed students (graded D and F) is 14.5% and 12.2%

respectively. In the academic year 2012-2013, the environment was integrated into the course.

We observed a reduction on the number of students who failed the course. At the same time,

an increase in the number of students with high scores (A and B) was observed. During the

academic year 2013-2014, an improved version of the environment was integrated into the

course. This results in a better performance in that academic year and the subsequent one

(2014-2015). Therefore, it is interesting to note that in all the three academic years of this study,

there was a significant increase in the number of students taking the course and writing the

final exam, and the failures are decreasing drastically. Moreover, another single most striking

observation to emerge from the result of our study is the percentage of students did not only

pass the course, but also obtained very promising grades. Figure.16 provides a graphical

representation of the results.

Figure.16

Percentage

2010-2011

Without the

environment

2011-2012

Without the

environment

2012-2013

With the

environment

2013-2014

With the

environment

2014-2015

With the

environment

Students taking the

final exam

79.8% 76.6% 91.4% 92.4% 96.5%

Score A 4.8% 7.3% 15% 13.7% 14.9%

Score B 20.5% 23.2% 35% 35.6% 39.2%

Score C 60.2% 57.3% 44.6% 48% 42.9%

Failed (D and F) 14.5% 12.2% 5.4% 2.7% 3%


55

From the data in Table 5, we can figure out the following improvement in the students’

performance and motivation:

1) Number of students who dropped the course was reduced from 20.2% and 23.4% in the

first two years (before integrating the environment into the course) to 8.6%, 7.6%, and 3.5%

in the last three years consecutively (after integrating the environment into the course).

This indicates that the students get more motivated and interested to study the subject.

2) Number of students with score A was more than doubled in the last three years compared

with the first two years.

3) Number of students with score B was improved from 20.5% and 23.2% in the first two

years to 35%, 35.6%, and 39.2% in the last three years consecutively.

4) Many students from the score C category are moved to score A or B category.

5) Number of failed students is dropped from 14.5% and 12.2% in the first two years to 5.4%,

2.7%, and 3% in the last three consecutive years.

DISCUSSION

This research was set out with the aim of designing and assessing the impact of

interactive learning environment for teaching and learning information and communication

theory and related science and engineering courses. In this study, comparing result of using

with that of not using the interactive environment in terms of students’ motivations and their

performance in the final examinations showed that the proposed method can make students

to confidently register for the course and participate in the learning activities from beginning

to the end of the course without thinking of whether to drop the course at the long run. Also

their performance during the course examinations (Table 4) indicates that this new teaching

method is more efficient than the traditional method. The finding further supports the idea of

using other interactive learning tools such as interactive whiteboards (Lopez, 2010), interactive

web-based tools (Cerra, 2014), interactive video (Kumar, 2010), or any visual representation

approach (Abdullah et.al, 2014) in teaching and learning of science and engineering courses.

Furthermore, not only the students will be happy with this recent development, research also

shows that teachers on the other hand have a positive view of using interactive learning tools.

The results observed in this research mirror those of the previous literatures (Dogru, 2014;

Podschuweit, et.al 2016; Tanahoung et.al 2009) that examined the effect of introducing

interactive learning environment at both secondary and tertiary institutions. One of the

significant implications of this finding is that of improving students motivation towards

learning and helping them to successfully pass their examinations with a high grades. It is

important also to understand that learning preference is one of the instruments to be

considered toward developing interactive learning environment, therefore further studies on

related topics should try to integrate the environment with a means of testing the learning

M. Hassan et al.

56

preference of a learner. The system was tested for a long period of time through several

consecutive semesters from 2010 to 2015. Hence the obtained data is more relatable.

Despite the positive side we prove in the research there is still few weak points. One of

the weak points of the system is that it does not support the use of mobile devices that is getting

more powerful, cheaper and at the same time more popular among young learners. This makes

the system to be of limited use through only usual personal computers. Further researches on

the current topic by extending the system to support mobile devices are therefore

recommended. One more point that requires much attention is that the current system might

not be suited for some learners specially learners with reflective learning preferences who

prefer reading text more than the style of learning by doing. More investigation in this regard

is needed and will be considered in future research.

CONCLUSION

This research work was undertaken to design an interactive learning environment for

teaching information and communication theory course and to evaluate its effect towards

improving students’ motivation and their performance in understanding the course and

during the assessment tests and examinations. The environment was designed and can be

used to assist students in understanding the course content using different learning and

teaching methods. The evaluation was undertaken within three consecutive academic

sessions, and the results were clearly presented both in tabular and graphical methods (Table

5 and Figure.16). The relevance of the proposed environment in teaching information and

communication theory is clearly supported by the findings of our experiments. The result of

this research supports the idea that:

1. Students should be taught based on their preferred method of teaching (their learning

styles).

2. The teaching for this course should be done from now on, using the proposed learning

environment.

Moreover, in general, the most obvious finding to emerge from this study is that there is

a need to integrate such learning environment in almost all computer science and engineering

courses. An implication of following the result and suggestion we proposed above is the

possibility that students performance will change drastically due to the high motivation they

can gain through using such learning environment.

Finally, it is recommended that further research be undertaken when more modules and

simulators are integrated into this learning environment, and perform more performance

evaluation with a higher number of students and within a very long period of time.


57

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